🤖 BCG GenAI Job Simulation - Financial Analysis & AI Chatbot

AI-powered financial chatbot that analyzes Microsoft, Tesla, and Apple's financial data from SEC 10-K filings.

Completed as part of Boston Consulting Group's (BCG) GenAI Virtual Job Simulation on Forage.

---

📋 Table of Contents

• About the Project
• Features
• Project Structure
• Installation
• Usage
• Example Queries
• Technologies Used
• What I Learned
• Future Enhancements
• Contact

---

🎯 About the Project

This project simulates real BCG consulting work and consists of two main tasks:

Task 1: Financial Data Extraction & Analysis 📊

• Extracted 3+ years of financial data from SEC 10-K filings for Microsoft, Tesla, and Apple
• Analyzed revenue growth trends and key financial metrics
• Created structured CSV dataset for AI chatbot integration
• Used Python with pandas for data manipulation and Jupyter Notebook for analysis

Task 2: AI Financial Chatbot Development 🤖

• Built a rule-based conversational chatbot using Python
• Integrated real financial data from Task 1
• Implemented intelligent query processing for natural language questions
• Delivers instant answers about company revenues, net income, and total assets

Completion Time: 2 days | Certification: BCG GenAI Job Simulation (Forage)

---

✨ Features

✅ Natural Language Processing - Ask questions in plain English
✅ Multi-Company Support - Analyzes Microsoft, Tesla, and Apple
✅ Real Financial Data - Based on actual SEC 10-K filings
✅ Instant Responses - Rule-based logic for fast answers
✅ Data Visualization - Jupyter Notebook with analysis insights
✅ User-Friendly Interface - Simple command-line chatbot

---

📁 Project Structure

File Name	Description
task-1.ipynb	Jupyter Notebook containing financial data extraction and analysis
financial_data.csv	Structured dataset with 3+ years of financial metrics (Revenue, Net Income, Assets)
financial_chatbot.py	Main Python chatbot application with query processing logic
README.md	Complete project documentation and user guide

Total Files: 4 | Lines of Code: ~150+ | Data Points: 45 financial metrics

---

🚀 Installation

Prerequisites

• Python 3.13 or higher
• pip (Python package manager)

Step 1: Clone the Repository

git clone https://github.com/kdeepak2001/BCG-GenAI-Financial-Chatbot.git cd BCG-GenAI-Financial-Chatbot

Step 2: Install Required Libraries

pip install pandas

Step 3: Verify Files

Ensure these files are in your directory:

• financial_chatbot.py
• financial_data.csv
• task-1.ipynb

---

💻 Usage

Running the Chatbot

1. Open your terminal/command prompt

2. Navigate to the project directory

3. Run the chatbot: python financial_chatbot.py

4. Start asking questions!

5. Type exit to quit the chatbot

Viewing Data Analysis (Task 1)

1. Open task-1.ipynb in Jupyter Notebook: jupyter notebook task-1.ipynb

2. Run all cells to see:

   • Financial data extraction process
   • Revenue growth analysis
   • Data cleaning and structuring

---

🔍 Example Queries

Try these questions with the chatbot:

🤔 Your question: What is Microsoft's revenue in 2024?

🤖 Answer: ✅ Microsoft's Total Revenue in 2024: $245,122 million

🤔 Your question: What is Tesla's net income in 2023?

🤖 Answer: ✅ Tesla's Net Income in 2023: $14,997 million

🤔 Your question: What are Apple's assets in 2024?

🤖 Answer: ✅ Apple's Total Assets in 2024: $364,980 million

🤔 Your question: exit

🤖 Answer: Thank you for using the Financial Chatbot! Goodbye! 👋

---

🛠️ Technologies Used

Technology	Purpose
Python 3.13	Core programming language
pandas	Data manipulation and CSV processing
Jupyter Notebook	Interactive data analysis environment
Regular Expressions (re)	Natural language query parsing
CSV	Financial data storage format

---

📚 What I Learned

Through this BCG GenAI simulation, I gained hands-on experience in:

✅ Financial Data Analysis - Extracting insights from SEC 10-K filings
✅ Natural Language Processing - Building rule-based chatbots
✅ Python Development - Writing production-ready code with error handling
✅ Data Engineering - Creating structured datasets from unstructured sources
✅ Consulting Skills - Simulating real BCG client project workflows
✅ Problem-Solving - Debugging CSV column name issues and data formatting

---

🚀 Future Enhancements

Potential improvements for this project:

• Add more companies (Google, Amazon, Meta)
• Implement web scraping for real-time data updates
• Build Streamlit web interface for browser-based chatbot
• Add data visualization (revenue growth charts)
• Integrate machine learning for sentiment analysis
• Deploy on Hugging Face Spaces for public access
• Add voice input/output using speech recognition
• Include historical trend predictions using time series analysis

---

📜 License

This project is created for educational purposes as part of the BCG GenAI Job Simulation on Forage.
Feel free to use this code for learning and portfolio purposes.

MIT License - Free to use with attribution.

---

📧 Contact

Your Name
🎓 Recent ECE Graduate (2024) | 🚀 AI Enthusiast | 💼 BCG GenAI Certified

• LinkedIn: Your LinkedIn Profile
• GitHub: @your-username
• Email: kalavadeepak2001@gmail.com

---

🏆 Certification

BCG GenAI Job Simulation - Completed October 2025 on Forage
Certificate: View Certificate

---

⭐ If you found this project helpful, please give it a star!

Built with 💡 passion during BCG GenAI Job Simulation

---

🤝 Acknowledgments

• Boston Consulting Group (BCG) - For creating this hands-on GenAI simulation
• Forage - For providing the platform and certification
• Python & pandas communities - For excellent documentation and support